Maps that are two-dimensional pictures of a particular geographical location take many forms. Maps exist that indicate the activities or demographics of people over mapped space. The information that is used to represent an aggregation of activities and demographics of people have largely come from public records and research studies such as crime statistics, traffic patterns, residential social-economic studies, census data, market surveys, tax assessment data. It is relatively new for such maps to mine data from the Internet contributed by individual users. Furthermore, information previously available was not a reliable predictor of future behavior by people in an area.
Data mining and machine learning by computers have been applied to everything from setting credit scores to making investments to predicting crimes. Some systems use natural language processing according to widely used human languages, such as English, Mandarin Chinese, Japanese, and German, to extract meaning and intent from raw data. Associating meanings with individual people, based on their expressions, may be an accurate predictor of their interests, future behavior, and even travel patterns. Mobile devices, and localized terminals, allow systems to process natural language meaning and intent for individuals across whatever locations they transit. However, such systems do not process natural language meanings and intents for particular locations and for the various individuals that pass through the locations.
Some experimental systems have used associations between geolocation tags and key words in Twitter tweets or other social media messages to make fascinating maps of sounds, smells, and emotions throughout cities. Such systems analyze data simplistically and provide data that is only useful for simple applications. Such systems use specific sets of keywords identified by researchers. However, mining social media message for keywords may not provide an accurate indication of the user's thoughts. Consider for example a user tweeting “the ball's in your court” may identify sports-related concepts rather than negotiation. The value of the thought map depends on the accuracy of identifying concepts.
Furthermore, keyword-based systems do not provide useful analysis across the dimension of time. In particular, they do not recognize cyclical patterns in time, such as expressions at particular times of day or days of week, and they do not recognize cyclical patterns in location, such as from bus stop to bus stop or house to house.